All or Nothing Results
On Sunday midday, May 17, 2026, the All or Nothing draw in Wisconsin marked a notable return: 01 03 05 07 10 12 13 18 19 21 22 reappeared in the draw after a -day drought. In a system where combinations should surface roughly once every 1 in 705,432 draws, an absence of this length stands out for anyone tracking long-horizon frequency trends.
Winning numbers for 2 draws on May 17, 2026 in Wisconsin.
Draw times: D, Evening.
Our take on the All or Nothing results
May 17, 2026All or Nothing report — Sunday midday, May 17, 2026: 01 03 05 07 10 12 13 18 19 21 22 shows a notable pattern
On Sunday midday, May 17, 2026, the All or Nothing draw in Wisconsin marked a notable return: 01 03 05 07 10 12 13 18 19 21 22 reappeared in the draw after a -day drought. In a system where combinations should surface roughly once every 1 in 705,432 draws, an absence of this length stands out for anyone tracking long-horizon frequency trends.
Overview
On Sunday midday, May 17, 2026, the All or Nothing draw in Wisconsin marked a notable return: 01 03 05 07 10 12 13 18 19 21 22 reappeared in the draw after a -day drought. In a system where combinations should surface roughly once every 1 in 705,432 draws, an absence of this length stands out for anyone tracking long-horizon frequency trends.
Combo Profile
Beyond the drought, the numbers show a clean structure: 11 distinct numbers with no repeats, spanning 1 to 22 (wide spread).
Why Droughts Matter
Extended absences like this provide context, not direction. They show how randomness behaves across large samples and help analysts quantify how often the system deviates from its baseline cadence.
Data Notes
This analysis uses the draw results recorded for Sunday midday, May 17, 2026 and compares them against the observed historical cadence for the game. This is descriptive, based on frequency tracking - not predictive modeling.
From Stepzero
At Stepzero, the priority is accuracy and context. This report is intended as a historical record entry, not a forecast.
Additional Context
Long-horizon measurement matters most when viewed across extended windows. As samples expand, the distribution becomes clearer and anomalies settle into their expected ranges. Long-horizon tracking is the only reliable way to separate short-term noise from persistent drift. By logging each outcome against its expected cadence, the system builds a distribution profile that becomes more stable as the sample grows.
Adding to the Long-Term Record
Over the broader record, today's outcome adds a new point to the dataset to the historical dataset. The record gains clarity as entries accumulate.